AI-Driven Simulation & Acceleration: Design and train advanced 3D machine learning models directly on CAD geometry and CAE mesh data to predict physical performance.
Physics-AI Integration: Utilize advanced physics-informed machine learning frameworks to develop AI surrogate models that respect fundamental laws of physics while delivering near-real-time simulation results.
Design Space Exploration: Integrate AI models into the generative design process, allowing design engineers to evaluate thousands of iterations instantly before committing to high-fidelity simulation.
High-Fidelity Simulation: Conduct complex, traditional CAE analyses (e.g., structural, thermal, crash, NVH, or aerodynamics) using industry-standard solvers.
HPC Utilization: Leverage and optimize workflows across High Performance Computing (HPC) clusters to efficiently scale both massive traditional simulations and the training of deep learning models.
Virtual Verification: Drive the "Virtual-First" verification strategy by correlating CAE models with physical test data, improving simulation accuracy to confidently reduce physical prototyping time and costs.
Initiative Ownership: Take full end-to-end ownership of AI-CAE integration projects, from initial research and proof-of-concept to production deployment.
Cross-Functional Teamwork: Act as a highly collaborative team player, seamlessly bridging the Mechanical/Aerospace Engineering, Data Science, and IT/HPC infrastructure teams.
Communication: Translate and communicate highly complex AI and physics concepts clearly and effectively to both technical peers and non-technical leadership.
Proactive Ownership: A self-starter mentality with a history of identifying process bottlenecks and successfully driving innovative solutions.

Requirements

Master’s degree or Ph.D. in Mechanical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, or a closely related field.
5+ years of industry experience in traditional CAE, computational mechanics, or CFD.
2+ years of hands-on experience applying Deep Learning / Machine Learning to engineering or physics problems.
Expertise with pre/post-processing tools (e.g., ANSA, HyperMesh, Meta) and industry-standard solvers.
Strong background in Classical CAE methods like FE, Implicit and explicit methods, CFD, CHT, and Multi Body Dynamics.
Proven track record of correlating simulation data with physical test results to validate engineering designs.
Proficiency in deep learning frameworks such as PyTorch.
Experience with Geometric Deep Learning and processing unstructured mesh/graph data.
Familiarity with physics-informed machine learning frameworks, digital twin environments, and engineering foundation models.
Strong programming skills in Python and C++; experience with GPU-accelerated computing is a strong plus.
Exceptional Communication: Proven ability to present complex technical presentations.

Benefits

Immediate medical, dental, vision and prescription drug coverage
Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
Vehicle discount program for employees and family members and management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
Paid time off and the option to purchase additional vacation time.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

3D machine learning modelsphysics-informed machine learningdeep learning frameworkspre/post-processing toolscomputational mechanicsCFDClassical CAE methodsPythonC++GPU-accelerated computing

Soft Skills

cross-functional teamworkcommunicationinitiative ownershipproactive ownership

Certifications

Master’s degreePh.D.